Genome-wide Lsd1 chromatin occupancy in myoblast C2C12 cells by chromatin immunoprecipitation using an Lsd1 antibody followed by massive parallel sequencing
Ontology highlight
ABSTRACT:
ORGANISM(S): Mus musculus
PROVIDER: GSE98134 | GEO | 2017/12/15
SECONDARY ACCESSION(S): PRJNA384063
REPOSITORIES: GEO
Similar Datasets
Project description:Purpose: The aim of this study is to identify the GR genome binding profile as well as that of Pol II, H3K27ac, H3K4me1, H3K4me3 and Ctcf in skeletal muscles. Methods: Libraries were prepared from immunoprecipitated DNA according to standard methods. ChIP-seq libraries were sequenced using a HiSeq 4000 (Illumina) and mapped to the mm10 reference genome using bowtie 2 (Langmead et al., 2009). Data were further analysed using the peak finding algorithm MACS 2 (Zhang et al., 2008) using input as control. All peaks with FDR greater than 0.1 % were excluded from further analysis. The uniquely mapped reads were used to generate the genome-wide intensity profiles, which were visualized using the IGV genome browser (Thorvaldsdottir et al., 2012). Results: HOMER (Heinz et al., 2010) was used to annotate peaks, to calculate overlaps between different peak files, and for motif searches. The genomic features (promoter, exon, intron, 3’ UTR, and intergenic regions) were defined and calculated using Refseq and HOMER. Genes annotated by HOMER were further used for a pathway analysis in WebGestalt (Heinz et al., 2010; Wang et al., 2013).
2020-10-01 | GSE142518 | GEO
Project description:Purpose: The aim of this study is to identify the GR genome binding profile as well as that of Pol II, H3K27ac, H3K4me1, H3K4me3 and Ctcf in skeletal muscles. Methods: Libraries were prepared from immunoprecipitated DNA according to standard methods. ChIP-seq libraries were sequenced using a HiSeq 4000 (Illumina) and mapped to the mm10 reference genome using bowtie 2 (Langmead et al., 2009). Data were further analysed using the peak finding algorithm MACS 2 (Zhang et al., 2008) using input as control. All peaks with FDR greater than 0.1 % were excluded from further analysis. The uniquely mapped reads were used to generate the genome-wide intensity profiles, which were visualized using the IGV genome browser (Thorvaldsdottir et al., 2012). Results: HOMER (Heinz et al., 2010) was used to annotate peaks, to calculate overlaps between different peak files, and for motif searches. The genomic features (promoter, exon, intron, 3’ UTR, and intergenic regions) were defined and calculated using Refseq and HOMER. Genes annotated by HOMER were further used for a pathway analysis in WebGestalt (Heinz et al., 2010; Wang et al., 2013).
2021-04-10 | GSE164543 | GEO
Project description:The aim of this study is to identify the Lsd1 genome binding profile in brown adipocytes. Purpose: The aim of this study is to identify the Lsd1 genome binding profile in brown adipocytes. Methods: Libraries were prepared from Lsd1-immunoprecipitated DNA according to standard methods. ChIP-seq libraries were sequenced using a HiSeq 2000 (Illumina) and mapped to the mm10 reference genome using bowtie 2 (Langmead et al., 2009). Data were further analysed using the peak finding algorithm MACS 1.41 (Zhang et al., 2008) using input as control. All peaks with FDR greater than 0.3 % were excluded from further analysis. The uniquely mapped reads were used to generate the genome-wide intensity profiles, which were visualized using the IGV genome browser (Thorvaldsdottir et al., 2012). Results: HOMER (Heinz et al., 2010) was used to annotate peaks, to calculate overlaps between different peak files, and for motif searches. The genomic features (promoter, exon, intron, 3’ UTR, and intergenic regions) were defined and calculated using Refseq and HOMER. Genes annotated by HOMER were further used for a pathway analysis in WebGestalt (Heinz et al., 2010; Wang et al., 2013). ChIP-seq analysis revealed that Lsd1 was located at the promoter of 11735 genes.
2016-10-27 | GSE81556 | GEO
Project description:Purpose: The goal of this study is to identify the role of HN1L protein as a transcription factor or co-factor in regulating TNBC cells. Methods: Due to the unavailability of a ChIP-grade HN1L antibody, we overexpressed FLAG-tagged HN1L in SUM159 cells and performed ChIP using anti-FLAG antibodies. ChIP DNA was prepared into libraries and sequenced by the Epigenomics Core of Weill Cornell Medical College using SR50 lane. Results: ChIP-Seq analysis began with mapping the sequenced reads to the genome. We utilized the Burrows-Wheeler Aligner (BWA) MEM algorithm to align the sequence reads against the human genome GRCh37/hg19 Assembly. We next used the Hypergoemetric Optimization of Motif Enrichment (HOMER) suite of tools to find and annotate peaks, and identify enriched motifs. HN1L showed 2,249 binding peaks from 10k bp upstream to 5k bp downstream of transcription start site (TSS). Conclusions: among the binding peaks 35 genes overlapped with a previously published BCSC gene signature, 10 genes overlapped with the HN1L knockdown gene signature and 8 genes are well established CSC transcription factors. When applying overlapped genes in the STRING10 pathway analysis database , a protein interaction network centered with STAT3 was obtained. STAT3 and FGFR2 peaks were validated by qPCR. Model-based Analysis of ChIP-Seq (MACS) was then used to confirm the peaks found by Hypergeometric Optimization of Motif EnRichment (HOMER) . Besides the binding peaks found in HOMER, another peak was called by MACS within LEPR , and was also validated by qPCR . These findings indicate that HN1L may act as a transcription factor through binding to enhancer regions of STAT3 and other STAT3 regulators.
2017-12-20 | GSE105446 | GEO
Project description:This data was generated by ENCODE. If you have questions about the data, contact the submitting laboratory directly (Florencia Pauli mailto:fpauli@hudsonalpha.org). If you have questions about the Genome Browser track associated with this data, contact ENCODE (mailto:genome@soe.ucsc.edu). The ChIP-Seq method was used to assay chromatin fragments bound by specific or general transcription factors as described below. DNA isolated by ChIP-Seq was size-selected (~225 bp) and sequenced. Short reads of 25-36 bp were mapped to the human reference genome, and enriched regions of high read density relative to a total input chromatin control reads were identified. The sequence reads with quality scores (fastq files) and alignment coordinates (BAM files) from these experiments are available for download. For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf Cells were grown according to the approved ENCODE cell culture protocols (http://hgwdev.cse.ucsc.edu/ENCODE/protocols/cell). Cross-linked chromatin was immunoprecipitated with an antibody. The Protein:DNA crosslinks were then reversed and the DNA fragments were recovered and sequenced. Please see protocol notes below and go to http://hudsonalpha.org/myers-lab/protocols for the most current version of the protocol. Biological replicates from each experiment were completed. Libraries were sequenced with an Illumina Genome Analyzer I or an Illumina Genome Analyzer IIx according to the manufacturer's recommendations. Sequence data produced by the Illumina data pipeline software were quality filtered and then mapped to NCBI Build37 (hg19) using the integrated Eland software; 32 nt of the sequence reads were used for alignment; up to two mismatches were tolerated; reads that mapped to multiple sites in the genome were discarded. To identify likely binding sites, peak calling was applied to the aligned sequence data sets using Model-based Analysis of Chip-Seq MACS (Zhang Y, et al., 2008) (http://liulab.dfci.harvard.edu/MACS/00README.html). MACS models the shift size of ChIP-Seq tags empirically, and uses it to improve the spatial resolution of predicted binding sites. MACS also uses a dynamic Poisson distribution to capture local biases in the genome, allowing for more robust predictions (Zhang Y, et al., 2008). Protocol Notes: Several changes and improvements were made to the original ChIP-Seq protocol (Jonshon et al.,2008). The major differences between protocols are the number of cells and magnetic beads used for IP, the method of sonication used to fragment DNA, and the number of cycles of PCR used to amplify the sequencing library. The most current protocol used by the Myers lab can be found at http://hudsonalpha.org/myers-lab/protocols. The protocol field for each file denotes the version of the protocol used as being PCR1x, PCR2x or a version number (for examples, v041610.1). The sequencing libraries labeled as PCR2x were made with two rounds of amplification (25 and 15 cycles) and those labeled as PCR1x were made with one 15-cycle round of amplification. These experiments were completed prior to January 2010 and were originally aligned to NCBI Build36 (hg18). They have been re-aligned to NCBI Build37 (hg19) with the Bowtie software (Langmead, et al., 2009) for this data release (http://bowtie-bio.sourceforge.net/index.shtml). The libraries labeled with a protocol version number were competed after January 2010 and were only aligned to NCBI Build37 (hg19). Please refer to the Myers Lab website (http://hudsonalpha.org/myers-lab/protocols) for details on each protocol version. Verification: The MACS (http://liulab.dfci.harvard.edu/MACS/00README.html) peak caller was used to call significant peaks on the individual replicates of a ChIP-Seq experiment. Afterwards, the irreproducible discovery rate (IDR) method, developed by Li et al. (submitted), was used to quantify the consistency between pairs of ranked peaks lists from replicates. The IDR methods uses a model that assumes that the ranked lists of peaks in a pair of replicates consist of two groups - a reproducible group and an irreproducible group. In general, the signals in the reproducible group are more consistent (i.e. with a larger rank correlation coefficient) and are ranked higher than the irreproducible group. The proportion of peaks that belong to the irreproducible component and the correlation of the reproducible component are estimated adaptively from the data. The model also provides an IDR score for each peak, which reflects the posterior probability of the peak belonging to the irreproducible group. The aligned reads were pooled from all replicates and the MACS peak caller was used to call significant peaks on the pooled data. Only datasets containing at least 100 peaks passing the IDR threshold are considered valid and submitted for release.
2011-09-29 | E-GEOD-32465 | biostudies-arrayexpress
Project description:Purpose: Assay for Transposase Accessible Chromatin with high-throughput sequencing (ATAC-seq) is a method for mapping chromatin accessibility genome-wide. The goals of this study are to investigate chromatin accessibility of C57BL/6J mice through ATAC-seq. Results: ATAC-seq reads were aligned to the UCSC mm10 reference genome with BWA-MEM. ATAC-seq peaks were called through MACS2 . Peaks were annotated and known transcription factor binding motifs were further analyzed in the ATAC-seq peaks by HOMER.
2020-07-08 | GSE153993 | GEO
Project description:We report genome-wide analysis of SF3B1-chromatin interactions in cell cycle synchronized HeLa cells. ChIP-Seq was performed on cell-cycle synchronized HeLa Cell for SF3B1 (G1S and G2M) in duplicates. RNA-Seq was performed on same cells, also in duplicates to determine changes in gene expression and RNA splicing. ChIP-Seq files were aligned to hg19 and peaks calls and comparisons performed by HOMER. RNA-Seq datasets were aligned using STAR aligner and differential expression determined by Cufflinks package. Alternative splicing was determined by rMATS algorithm
2018-05-15 | GSE108347 | GEO
Project description:This study was designed to explore the role of LINC02015 in regulating biological behaviors of vascular smooth muscle cells (VSMCs) and investigate the interacting targets of LINC02015 with high-throughput techniques and bioinformatics analysis. Chromatin isolation by RNA purification (ChIRP) sequencing was applied to explore the interaction relationship among LncRNA, RNA-binding protein and DNA. Localization of the active binding sites was specified by high-throughput sequencing with STAR2. Peak calling analysis was applied to spot enriched gene peaks with MACS.
2021-11-16 | GSE188751 | GEO
Project description:For RNA-seq,mRNA profiles of Bone marrow derived macrophages(BMDMs) of wild-type(WT) and Zmynd8 knockout(Zmynd8 cKO),p65 KO + p65,p65 KO + K122R in Raw264.7 cells,Zmynd8 KO + Zmynd8,Zmynd8 KO + D/E-A in Raw264.7 cells were generated from GUANGZHOU RIBOBIO,using Illumina Hiseq.The sequence reads that passed quality filters were analyzed at the transcript isoform level with two methods: Burrows–Wheeler Aligner (BWA) followed by ANOVA (ANOVA) and TopHat followed by Cufflinks. qRT–PCR validation was performed using TaqMan and SYBR Green assays.For ChIP-seq,BMDMs or Raw264.7 cells (about 1x107 cells) were fixed with 1% methanol-free formaldehyde at room temperature for 10 min, followed by quenching with 125mM glycine,and chromatin immunoprecipitated DNA was send to Novogene,using Nova-PE150.The FASTQ data were mapped to the mouse genome (mm10) using Bowtie, and significant enrichments were identified by MACS2.0 using Broad Peak mode with p-value ≤1*10 -5, FDR ≤ 0.01 as a cutoff to call peaks from the aligned results.For ATAC-seq,BMDMs were send to Shanghai Da Che Biotechnology Company,and the FASTQ data were mapped to the mouse genome (mm10) using Bowtie, and significant enrichments were identified by MACS2.0 using Broad Peak mode with p-value ≤1*10 -5, FDR ≤ 0.01 as a cutoff to call peaks from the aligned results.
2021-10-16 | GSE169470 | GEO
Project description:Transcription factor SOX4 has been implicated in skeletal myoblast differentiation through the regulation of Cald1 gene expression; however, the detailed molecular mechanism underlying this process is largely unknown. Here, we demonstrate that SOX4 acetylation at lysine 95 by KAT5 (also known as Tip60) is essential for Cald1 promoter activity at the onset of C2C12 myoblast differentiation. KAT5 chromodomain was found to facilitate SOX4 recruitment to the Cald1 promoter, which is involved in chromatin remodeling at the promoter. Chromatin occupancy analysis of SOX4, KAT5, and HDAC1 indicated that the expression of putative SOX4 target genes during C2C12 myoblast differentiation is specifically regulated by the molecular switching of the co-activator KAT5 and the co-repressor HDAC1 on SOX4 transcriptional activation.
| S-EPMC4558493 | biostudies-literature